Interactions with Streptococcus

Data Source:

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There are several ways of doing association. At the bottom is trying linear regression on the percentiles. Doing linear regression of actual counts produce few results. For a blog post describing the process see Bacteria interacting with bacteria.

Above this is the association using Kalthrop-Moldrup Limits (Patent Pending) to determine non parametric association between High Levels and Low Levels. High levels may cause changes in other bacteria (for example, due to amount of natural antibiotics released) but no effect with low levels. Similarly the reverse.

Chi2 is truncated to 10000. Example Expected Count was 13.4, observed was 540


Calculated from Fy(y)=a + (slope) Fx(x). F(x) are custom monotonic functions. See this post for technical notes on computation

RankBacteriaSlope
Relative Impact
Percentage ExplainedImpact
class Bacilli 59.1% 35.1% 20.7%
family Streptococcaceae 97.1% 95% 92.3%
no rank unclassified Rothia (in: high G+C Gram-positive bacteria) 48.1% 22% 10.6%
no rank unclassified Streptococcus 57.5% 36.7% 21.1%
order Lactobacillales 70.8% 48% 33.9%
species Bacteroides sp. HPS0048 60.8% 31.2% 19%
species Rothia sp. THG-N7 47.4% 21.3% 10.1%
species Streptococcus australis 53.7% 25.5% 13.7%
species Streptococcus parasanguinis 65.9% 36.7% 24.2%
species Streptococcus pasteurianus 56.5% 32.2% 18.2%
species Streptococcus sp. 2011_Oral_MS_A3 78.7% 56.3% 44.3%
species Streptococcus sp. BS35a 61.3% 38.7% 23.8%
species Streptococcus thermophilus 68% 40.8% 27.8%
species Veillonella sp. AS16 50.9% 24.7% 12.6%

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